class_docstr: Dataset of Reddit comments with one or more emotion labels.
docstr: Yields examples.
  
  Each example contains a text input with the relevant emotion labels.
  
  Args:
    filename: the path of the file to be read for this split.
  
  Yields:
    A dictionary of features, containing the comment text and, for each
    emotions label, 0/1 depending on whether is it a label for the input.
docstr_tok: ["Yields","examples",".","Each","example","contains","a","text","input","with","the","relevant","emotion","labels",".","Args",":","filename",":","the","path","of","the","file","to","be","read","for","this","split",".","Yields",":","A","dictionary","of","features",",","containing","the","comment","text","and",",","for","each","emotions","label",",","0\/1","depending","on","whether","is","it","a","label","for","the","input","."]
code: def _generate_examples(self, filename):
      """Yields examples.
  
      Each example contains a text input with the relevant emotion labels.
  
      Args:
        filename: the path of the file to be read for this split.
  
      Yields:
        A dictionary of features, containing the comment text and, for each
        emotions label, 0/1 depending on whether is it a label for the input.
      """
      fieldnames = ["comment_text", "emotion_ids", "comment_id"]
      with epath.Path(filename).open() as f:
          reader = csv.DictReader(
              f, fieldnames=fieldnames, delimiter="\t"
          )
          for row in reader:
              example = self._parse_row_as_example(row)
              if example:
                  yield row["comment_id"], example
  
file_path: datasets/tensorflow_datasets/text/goemotions.py
imports: import csv
  from etils import epath
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 4 for calling the Goemotions obj's underlying member method code with example usage: obj._generate_examples(filename) without return types